Abstract
The paper develops a receding horizon control strategy to guarantee closed loop convergence and feasibility in respect of soft constraints. Earlier work (Cannon et al., 2007) presented results addressing closed loop stability in the case of multiplicative uncertainty only. The present paper extends these results to the more general case of additive and multiplicative uncertainty and proposes a method of handling probabilistic constraints. The results are illustrated by a design study considering control of a wind turbine in order to maximize power capture subject to constraints on fatigue damage.
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